Tests for Changes in Count Time Series Models With Exogenous Covariates
ABSTRACT We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH‐X).
Šárka Hudecová, Marie Hušková
wiley +1 more source
A method for constructing digital twins of CNC machine tools feed systems based on hybrid mechanism-data. [PDF]
Zhao R, Huang H, Mei L.
europepmc +1 more source
Time‐Varying Dispersion Integer‐Valued GARCH Models
ABSTRACT We introduce a general class of INteger‐valued Generalized AutoRegressive Conditionally Heteroscedastic (INGARCH) processes by allowing simultaneously time‐varying mean and dispersion parameters. We call such models time‐varying dispersion INGARCH (tv‐DINGARCH) models.
Wagner Barreto‐Souza +3 more
wiley +1 more source
Batch Bayesian auto-tuning for nonlinear Kalman estimators. [PDF]
Freitas Iglesias C, Bolic M.
europepmc +1 more source
High impedance faults detection in power distribution networks using rogowski coils, kalman filtering, least-squares and non-recursive DFT computation engines. [PDF]
Ali ZM +3 more
europepmc +1 more source
A Novel VSS-LMS Algorithm Based on Modified Versoria Function for Anti-Jamming. [PDF]
Tian B, Feng Y, Liu F, Song B, Guo S.
europepmc +1 more source
A fractional model based on caputo derivative for tuberculosis transmission using real data from Kenya. [PDF]
Bhatia B +4 more
europepmc +1 more source
Dual-Objective Optimization of G<sup>3</sup>-Continuous Quintic B-Spline Trajectories for Robotic Ultrasonic Testing. [PDF]
Ma P, Xu C.
europepmc +1 more source
Research on the estimation method of crop net primary productivity based on improved CASA model. [PDF]
Li W +7 more
europepmc +1 more source
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Statistically linearized recursive least squares
2010 IEEE International Workshop on Machine Learning for Signal Processing, 2010This article proposes a new interpretation of the sigmapoint kalman filter (SPKF) for parameter estimation as being a statistically linearized recursive least-squares algorithm. This gives new insight on the SPKF for parameter estimation and particularly this provides an alternative proof for a result of Van der Merwe. On the other hand, it legitimates
Geist, Matthieu, Pietquin, Olivier
openaire +2 more sources

